Autonomous water pollution source tracking system using fish robot
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
Mapping system of water pollution by autonomous fish robots
ROCOM'07 Proceedings of the 7th WSEAS International Conference on Robotics, Control & Manufacturing Technology
Neurofuzzy networks with nonlinear quantum learning
IEEE Transactions on Fuzzy Systems
Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Research on Error Compensation for Oil Drilling Angle Based on ANFIS
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Fuzzy Sets and Systems
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
ANFIS-based wireless LAN indoor positioning algorithm
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Modeling and simulation of combinational CMOS logic circuits by ANFIS
Microelectronics Journal
Forecasting coal and rock dynamic disaster based on adaptive neuro-fuzzy inference system
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Fuzzy decision making on direction changes of water pollution monitoring underwater robots
EE'07 Proceedings of the 2nd IASME/WSEAS international conference on Energy and environment
Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions
Expert Systems with Applications: An International Journal
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
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A useful neural network paradigm for the solution of function approximation problems is represented by adaptive neuro-fuzzy inference systems (ANFIS). Data driven procedures for the synthesis of ANFIS networks are typically based on clustering a training set of numerical samples of the unknown function to be approximated. Some serious drawbacks often affect the clustering algorithms adopted in this context, according to the particular data space where they are applied. To overcome such problems, we propose a new ANFIS synthesis procedure where clustering is applied in the joint input-output data space. Using this approach, it is possible to determine the consequent part of Sugeno first-order rules and therefore the hyperplanes characterizing the local structure of the function to be approximated. Successively, the fuzzy antecedent part of each rule is determined using a particular fuzzy min-max classifier, which is based on the adaptive resolution mechanism. The generalization capability of the resulting ANFIS architecture is optimized using a constructive procedure for the automatic determination of the optimal number of rules. Simulation tests and comparisons with respect to other neuro-fuzzy techniques are discussed in the paper, in order to assess the efficiency of the proposed approach.